Spatial Planning and Connectivity Corpus - Technical Background Report

IPBES Spatial Planning and Connectivity Assessment

Abstract

To Be added

Disclaimer

Contributors

Assessment Experts

• xxx, yyy

Data and Knowledge tsu

• Niamir, Aidin

Working Title

IPBES_SPC_Corpus

Code repo

Github repository

Build No: 55

Introduction

The literature search for the Spatial Planning and Connectivity assessment corpus was conducted using search terms provided by the experts and refined in co-operation with the IPBES task force for data and knowledge management. The search was conducted using OpenAlex, scripted from R to use the OpenAlex API. Search terms for the following searches were defined:

• Spatial Planning and Connectivity,
• Nature / Environment
• additional search terms for specific corpora

To assess the quality of the corpus, sets of key papers were selected by the experts to verify if these are in the corpus.

The following terminology is used in this document:

• Corpus: A body of works as based on a search on OpenAlex
• Spatial Planning and Connectivity Assessment Corpus: Short: SPC corpus; The corpus resulting from the search terms TO BE ADDED
• work: terminology used for a single document in their dataset. Each work has a unique OpenAlex id, but not necesarily a DOI.

The following searches are conducted on Title and Abstract only to account for fluctuating availability of full text searches and make the search more focussed..

Schematic Overview

Overview

flowchart TD
    Start([Start literature search]) --> SPC["spc_corpus.yaml<br/>Assemble base SPC corpus"]
    click SPC "./input/search_terms/spc_corpus.yaml" "Open spc_corpus.yaml"
    SPC --> SPC_list["spc keyword set<br/>(planning & connectivity terms)"]
    SPC --> NATURE_list["nature keyword set<br/>(environmental context terms)"]
    SPC_list --> BaseQuery["Level 1 query<br/>spc terms AND nature dictionary"]
    NATURE_list --> BaseQuery

    BaseQuery --> ChapterSelect{Apply chapter / theme refinements}

    ChapterSelect --> CH1["Chapter 1<br/>chapter_1.v1.yaml<br/>Governance & planning principles"]
    click CH1 "./input/search_terms/chapter_1.v1.yaml" "Open chapter_1.v1.yaml"
    ChapterSelect --> CH2["Chapter 2<br/>chapter_2.yaml + chapter_2_add.yaml + chapter_2_sdg.yaml<br/>GBF targets, nexus themes, SDGs"]
    click CH2 "./input/search_terms/chapter_2.yaml" "Open chapter_2.yaml"
    ChapterSelect --> CH3["Chapter 3<br/>chapter_3.yaml<br/>Restoration & conservation planning"]
    click CH3 "./input/search_terms/chapter_3.yaml" "Open chapter_3.yaml"
    ChapterSelect --> CH4["Chapter 4<br/>chapter_4.yaml<br/>Connectivity evidence & metrics"]
    click CH4 "./input/search_terms/chapter_4.yaml" "Open chapter_4.yaml"
    ChapterSelect --> CH5["Chapter 5<br/>chapter_5_* files<br/>Foresight & futures (sections, themes, cross-cutting)"]
    click CH5 "./input/search_terms/Chapter_5_1_2.yaml" "Open Chapter 5 search terms"
    ChapterSelect --> CH6["Chapter 6<br/>chapter_6.yaml (+ chapter_6_r2.yaml optional)<br/>Enabling environment"]
    click CH6 "./input/search_terms/chapter_6.yaml" "Open chapter_6.yaml"

Chapter 1

flowchart LR
    Start([SPC Corpus]) --> Ch1["chapter_1.v1.yaml<br/>Level 2 refinements"]
    click Ch1 "./input/search_terms/chapter_1.v1.yaml" "Open chapter_1.v1.yaml"

    subgraph Chapter1Sets["Chapter 1 thematic searches"]
        direction TB
        C1_1["Set 1:<br/>land/spatial planning<br/>+ biodiversity goals<br/>+ societal needs/values"]
        C1_2["Set 2:<br/>adaptive/scenario planning<br/>+ monitoring/feedback"]
        C1_3["Set 3:<br/>evidence & precaution<br/>+ ILK knowledge base"]
        C1_4["Set 4:<br/>multilevel/transparent governance<br/>+ customary coherence"]
        C1_5["Set 5:<br/>participatory planning<br/>+ co-design / engagement"]
        C1_6["Set 6:<br/>equity / rights / tenure<br/>+ justice outcomes"]
        C1_7["Set 7:<br/>connectivity (land-sea/cross-scale)<br/>+ nexus & climate links"]
    end

    Ch1 --> C1_1
    Ch1 --> C1_2
    Ch1 --> C1_3
    Ch1 --> C1_4
    Ch1 --> C1_5
    Ch1 --> C1_6
    Ch1 --> C1_7

Chapter 2

flowchart LR
    Start([SPC Corpus]) --> Ch2L2["chapter_2.yaml<br/>Level 2 GBF bundles"]
    click Ch2L2 "../search_terms/chapter_2.yaml" "Open chapter_2.yaml"

    subgraph L1["GBF contexts & targets"]
        direction TB
        GBF_Urban["GBF-1 Urban"]
        GBF_Rural["GBF-1 Rural"]
        GBF_Fresh["GBF-1 Freshwater"]
        GBF_Marine["GBF-1 Marine"]
        GBF_Restore["GBF-2 Ecosystem restoration"]
        T3["Target 3"]
        T4["Target 4"]
        T5["Target 5"]
        T6["Target 6"]
        T7["Target 7"]
        T8["Target 8"]
        T9["Target 9"]
        T10["Target 10"]
        T11["Target 11"]
        T12["Target 12"]
        T13["Target 13"]
        T14["Target 14"]
        T15["Target 15"]
        T16["Target 16"]
        T17["Target 17"]
        T18["Target 18"]
        T19["Target 19"]
        T20["Target 20"]
        T21["Target 21"]
        T22["Target 22"]
        T23["Target 23"]
        REL["Spatial Planning Related"]
    end
    Ch2L2 --> GBF_Urban
    Ch2L2 --> GBF_Rural
    Ch2L2 --> GBF_Fresh
    Ch2L2 --> GBF_Marine
    Ch2L2 --> GBF_Restore
    Ch2L2 --> T3
    Ch2L2 --> T4
    Ch2L2 --> T5
    Ch2L2 --> T6
    Ch2L2 --> T7
    Ch2L2 --> T8
    Ch2L2 --> T9
    Ch2L2 --> T10
    Ch2L2 --> T11
    Ch2L2 --> T12
    Ch2L2 --> T13
    Ch2L2 --> T14
    Ch2L2 --> T15
    Ch2L2 --> T16
    Ch2L2 --> T17
    Ch2L2 --> T18
    Ch2L2 --> T19
    Ch2L2 --> T20
    Ch2L2 --> T21
    Ch2L2 --> T22
    Ch2L2 --> T23
    Ch2L2 --> REL

    L1 --> Ch2L3["chapter_2_add.yaml<br/>Level 3 nexus themes"]
    click Ch2L3 "../search_terms/chapter_2_add.yaml" "Open chapter_2_add.yaml"
    subgraph NexusSets["Nexus add-ons"]
        direction TB
        Nexus_Water["Water"]
        Nexus_Food["Food"]
        Nexus_Health["Health"]
        Nexus_Climate["Climate"]
    end
    Ch2L3 --> Nexus_Water
    Ch2L3 --> Nexus_Food
    Ch2L3 --> Nexus_Health
    Ch2L3 --> Nexus_Climate

    L1 --> Ch2L4["chapter_2_sdg.yaml<br/>Level 4 SDG filters"]
    click Ch2L4 "../search_terms/chapter_2_sdg.yaml" "Open chapter_2_sdg.yaml"
    subgraph SDGSets["SDG goal filters"]
        direction TB
        SDG1["SDG 1"]
        SDG2["SDG 2"]
        SDG3["SDG 3"]
        SDG4["SDG 4"]
        SDG5["SDG 5"]
        SDG6["SDG 6"]
        SDG7["SDG 7"]
        SDG8["SDG 8"]
        SDG9["SDG 9"]
        SDG10["SDG 10"]
        SDG11["SDG 11"]
        SDG12["SDG 12"]
        SDG13["SDG 13"]
        SDG14["SDG 14"]
        SDG15["SDG 15"]
        SDG16["SDG 16"]
        SDG17["SDG 17"]
    end
    Ch2L4 --> SDG1
    Ch2L4 --> SDG2
    Ch2L4 --> SDG3
    Ch2L4 --> SDG4
    Ch2L4 --> SDG5
    Ch2L4 --> SDG6
    Ch2L4 --> SDG7
    Ch2L4 --> SDG8
    Ch2L4 --> SDG9
    Ch2L4 --> SDG10
    Ch2L4 --> SDG11
    Ch2L4 --> SDG12
    Ch2L4 --> SDG13
    Ch2L4 --> SDG14
    Ch2L4 --> SDG15
    Ch2L4 --> SDG16
    Ch2L4 --> SDG17

Chapter 3

flowchart LR
    Start([SPC Corpus]) --> Ch3["chapter_3.yaml<br/>Level 2 refinements"]
    click Ch3 "./input/search_terms/chapter_3.yaml" "Open chapter_3.yaml"

    subgraph Chapter3Sets["Chapter 3 searches"]
        direction TB
        C3_1["Set 1:<br/>Protected/OECM + NBSAP + cases<br/>+ spatial prioritization + regional scales"]
        C3_2["Set 2:<br/>Restoration types + inclusivity & ILK"]
        C3_3["Set 3:<br/>Restoration planning + connectivity + resilience"]
        C3_4["Set 4:<br/>Systematic conservation planning / gap analysis"]
        C3_5["Set 5:<br/>Protected area & connectivity planning"]
        C3_6["Set 6:<br/>Landscape/species/corridor networks"]
        C3_7["Set 7:<br/>Conservation planning + ecosystem services"]
        C3_8["Set 8:<br/>Participatory conservation mapping"]
        C3_9["Set 9:<br/>Conservation effectiveness + spatial planning"]
        C3_10["Set 10:<br/>Adaptive management under global change drivers"]
    end
    Ch3 --> C3_1
    Ch3 --> C3_2
    Ch3 --> C3_3
    Ch3 --> C3_4
    Ch3 --> C3_5
    Ch3 --> C3_6
    Ch3 --> C3_7
    Ch3 --> C3_8
    Ch3 --> C3_9
    Ch3 --> C3_10

Chapter 4

flowchart LR
    Start([SPC Corpus]) --> Ch4["chapter_4.yaml<br/>Level 2 refinements"]
    click Ch4 "./input/search_terms/chapter_4.yaml" "Open chapter_4.yaml"

    subgraph Chapter4Sets["Chapter 4 searches"]
        direction TB
        C4_1["Set 1:<br/>Connectivity review articles"]
        C4_2["Set 2:<br/>Connectivity benefits vs risks<br/>+ ecosystem services"]
        C4_3["Set 3:<br/>Structural vs functional connectivity"]
        C4_4["Set 4:<br/>Connectivity modelling toolkits"]
        C4_5["Set 5:<br/>Connectivity indicators & metrics"]
        C4_6["Set 6:<br/>Policy & governance integration"]
        C4_7["Set 7:<br/>Multilevel / transboundary governance<br/>+ IPLC inclusion + land tenure"]
        C4_8["Set 8:<br/>Movement ecology<br/>(dispersal / migration / permeability)"]
    end
    Ch4 --> C4_1
    Ch4 --> C4_2
    Ch4 --> C4_3
    Ch4 --> C4_4
    Ch4 --> C4_5
    Ch4 --> C4_6
    Ch4 --> C4_7
    Ch4 --> C4_8

Chapter 5

flowchart LR
    Start([SPC Corpus]) --> Ch5L2["Chapter_5_1_2.yaml<br/>Level 2 foresight framing"]
    click Ch5L2 "./input/search_terms/Chapter_5_1_2.yaml" "Open Chapter_5_1_2.yaml"

    subgraph L2Sets["Section 5.1–5.2 searches"]
        direction TB
        C5_1["Set 1:<br/>Future + project*/predict*/scenario"]
        C5_2["Set 2:<br/>Future + pathway*/narrative*/vision*"]
        C5_3["Set 3:<br/>Future-proof*/anticipat*/scenario planning"]
        C5_4["Set 4:<br/>Foresight*/backcasting/simulat*/trend*"]
        C5_5["Set 5:<br/>Model* AND scenario"]
    end
    Ch5L2 --> C5_1
    Ch5L2 --> C5_2
    Ch5L2 --> C5_3
    Ch5L2 --> C5_4
    Ch5L2 --> C5_5

    Start --> Ch5L3["chapter_5_3.yaml<br/>Level 3 – Drivers of change"]
    click Ch5L3 "./input/search_terms/chapter_5_3.yaml" "Open chapter_5_3.yaml"
    subgraph L3Sets["Section 5.3 searches"]
        direction TB
        C5_3a["Set 1:<br/>Drivers of change"]
        C5_3b["Set 2:<br/>Driver modelling approaches"]
        C5_3c["Set 3:<br/>Driver gaps"]
    end
    Ch5L3 --> C5_3a
    Ch5L3 --> C5_3b
    Ch5L3 --> C5_3c

    Start --> Ch5L4["chapter_5_4.yaml<br/>Level 4 – Synergies & trade-offs"]
    click Ch5L4 "./input/search_terms/chapter_5_4.yaml" "Open chapter_5_4.yaml"
    subgraph L4Sets["Section 5.4 searches"]
        direction TB
        C5_4a["Set 1:<br/>Interactions (synergy/trade-off/nexus)"]
        C5_4b["Set 2:<br/>Response options (integrated planning / NbS)"]
        C5_4c["Set 3:<br/>Cross-scale synergy & trade-off terms"]
    end
    Ch5L4 --> C5_4a
    Ch5L4 --> C5_4b
    Ch5L4 --> C5_4c

    Start --> Ch5L5["chapter_5_5.yaml<br/>Level 5 – Uncertainty & risk"]
    click Ch5L5 "./input/search_terms/chapter_5_5.yaml" "Open chapter_5_5.yaml"
    subgraph L5Sets["Section 5.5 searches"]
        direction TB
        C5_5a["Set 1:<br/>Adaptive / transformative management"]
        C5_5b["Set 2:<br/>Uncertainty quantification"]
        C5_5c["Set 3:<br/>Tipping points & thresholds"]
        C5_5d["Set 4:<br/>Cascading risks & precaution"]
    end
    Ch5L5 --> C5_5a
    Ch5L5 --> C5_5b
    Ch5L5 --> C5_5c
    Ch5L5 --> C5_5d

    Start --> Ch5L6["chapter_5_6.yaml<br/>Level 6 – Knowledge to action"]
    click Ch5L6 "./input/search_terms/chapter_5_6.yaml" "Open chapter_5_6.yaml"
    subgraph L6Sets["Section 5.6 searches"]
        direction TB
        C5_6a["Set 1:<br/>Science-policy-practice pathways"]
        C5_6b["Set 2:<br/>ILK integration & community planning"]
        C5_6c["Set 3:<br/>Enabling factors & coordination"]
        C5_6d["Set 4:<br/>Shocks, surprises, uncertainties"]
    end
    Ch5L6 --> C5_6a
    Ch5L6 --> C5_6b
    Ch5L6 --> C5_6c
    Ch5L6 --> C5_6d

    Start --> Ch5CC["chapter_5_cc.yaml<br/>Cross-cutting themes"]
    click Ch5CC "./input/search_terms/chapter_5_cc.yaml" "Open chapter_5_cc.yaml"
    subgraph CCSets["Cross-cutting searches"]
        direction TB
        CC1["Set 1:<br/>Scales & telecoupling"]
        CC2["Set 2:<br/>Co-production & inclusion"]
    end
    Ch5CC --> CC1
    Ch5CC --> CC2

Chapter 6

flowchart LR
    Start([SPC Corpus]) --> Ch6["chapter_6.yaml<br/>Level 2 refinements"]
    click Ch6 "./input/search_terms/chapter_6.yaml" "Open chapter_6.yaml"

    subgraph Chapter6Sets["Chapter 6 searches"]
        direction TB
        C6_1["Set 1:<br/>Institutional & governance structures"]
        C6_2["Set 2:<br/>Political & strategic leadership"]
        C6_3["Set 3:<br/>Socio-cultural & stakeholder engagement"]
        C6_4["Set 4:<br/>Collaboration, trust & networks"]
        C6_5["Set 5:<br/>Financial & economic mechanisms"]
        C6_6["Set 6:<br/>Legal & policy frameworks"]
        C6_7["Set 7:<br/>Human & institutional capacity"]
        C6_8["Set 8:<br/>Data, knowledge & decision support"]
        C6_9["Set 9:<br/>Ecological & spatial planning tools"]
        C6_10["Set 10:<br/>Cross-cutting process enablers"]
    end
    Ch6 --> C6_1
    Ch6 --> C6_2
    Ch6 --> C6_3
    Ch6 --> C6_4
    Ch6 --> C6_5
    Ch6 --> C6_6
    Ch6 --> C6_7
    Ch6 --> C6_8
    Ch6 --> C6_9
    Ch6 --> C6_10

Ch6R2["chapter_6_r2.yaml<br/>Optional Level 3 filter"]
Chapter6Sets --> Ch6R2
click Ch6R2 "./input/search_terms/chapter_6_r2.yaml" "Open chapter_6_r2.yaml"
subgraph Chapter6Case["Chapter 6 searches"]
    R2["Case-study keywords<br/>(case stud*, example*, initiative*, etc.)"]
end
Ch6R2 --> R2

Type Selsection

OpenAlex contains more then 270 million works of different types. The following table shows and explains the available types and highlights which are selected to be included in the SPC Corpus.

params$types |>
  knitr::kable(
    caption = "OpenAlex Work Types and Inclusion in the SPC Corpus",
    booktabs = TRUE,
    align = c("l", "l", "l", "c")
  ) # |>

OpenAlex Work Types and Inclusion in the SPC Corpus

Type	Description	Included
article	Scholarly journal articles and related periodical works	TRUE
book	Monographs and other long-form published books	TRUE
book-chapter	Chapters published within edited books or proceedings	TRUE
dissertation	Doctoral or master level theses and dissertations	TRUE
preprint	Pre-publication manuscripts shared prior to peer review	TRUE
report	Technical, institutional, or policy reports	TRUE
review	Narrative or systematic review articles	TRUE
dataset	Published datasets and structured data releases	FALSE
editorial	Editorials and editor introductions	FALSE
erratum	Published corrections to previously released works	FALSE
grant	Summaries or descriptions of grant-funded projects	FALSE
letter	Correspondence, commentaries, and short letters	FALSE
libguides	Library research guides and curated bibliographies	FALSE
other	Works that do not align with a more specific OpenAlex type	FALSE
paratext	Prefaces, introductions, indexes, and other paratextual items	FALSE
peer-review	Formal peer review reports and evaluations	FALSE
reference-entry	Encyclopaedia or dictionary reference entries	FALSE
retraction	Notices retracting previously published works	FALSE
standard	Standards, protocols, and technical specifications	FALSE
supplementary-materials	Supplementary files accompanying primary publications	FALSE

# kableExtra::kable_styling(
#     full_width = FALSE,
#     position = "left"
# )

Methods

Assess of Individual Terms in spc and nature search terms

This assessment is done on the whole of the OpenAlex corpus and only filtered for types and not for the date range.

fn <- file.path(params$output_dir, "searchterm_assessment_spcc.rds")

if (!file.exists(fn)) {
  result <- list(
    spc = assess_search_term_both(
      st = params$search_terms$spc,
      and_term = st(params$search_terms$nature),
      types = params$types_filter,
      verbose = FALSE
    ),
    nature = assess_search_term_both(
      st = params$search_terms$nature,
      and_term = st(params$search_terms$spc),
      types = params$types_filter,
      verbose = FALSE
    )
  ) |>
    saveRDS(fn)
}

Get Key Paper

Here we get key papers in a parquet database which is partitioned by:

• found_in: the search term or openalex which is used as the filter, i.e. the key paper occurs in corpus which would result from the search term
• id_used: the id used to testing,m either the OpenAlex id (id) or the doi (doi)
• page: only for processing reasons

No filtering, neither by type nor by publication year is done.

The other columns are as returned by the OpenAlex API.

#|

fn <- file.path(params$keyworks, "parquet")
if (!dir.exists(fn)) {
  # oa
  st <- list(
    spc = params$search_terms$spc |>
      paste0(collapse = " "),
    nature = params$search_terms$nature |>
      paste0(collapse = " ")
  )
  st$spcc <- paste0("(", st$spc, ") AND (", st$nature, ")")

  dois <- params$key_papers$goldstandard$DOI[
    params$key_papers$goldstandard$DOI != ""
  ]
  ids <- params$key_papers$goldstandard$openalex_id[
    params$key_papers$goldstandard$openalex_id != ""
  ]

  ### KP in OpenAlex
  openalexPro::pro_query(
    doi = dois,
    chunk_limit = 50
  ) |>
    openalexPro::pro_request(
      output = file.path(fn, "..", "json_doi")
    ) |>
    openalexPro::pro_request_jsonl(
      output = file.path(fn, "..", "jsonl_doi"),
      delete_input = TRUE
    ) |>
    openalexPro::pro_request_jsonl_parquet(
      output = file.path(fn, "found_in=openalex", "id_used=doi"),
      delete_input = TRUE
    )

  openalexPro::pro_query(
    id = ids,
    multiple_id = TRUE,
    chunk_limit = 50
  ) |>
    openalexPro::pro_request(
      output = file.path(fn, "..", "json_oa_id")
    ) |>
    openalexPro::pro_request_jsonl(
      output = file.path(fn, "..", "jsonl_oa_id"),
      delete_input = TRUE
    ) |>
    openalexPro::pro_request_jsonl_parquet(
      output = file.path(fn, "found_in=openalex", "id_used=oa_id"),
      delete_input = TRUE
    )

  ### KP in spc
  openalexPro::pro_query(
    title_and_abstract.search = st$spc,
    doi = dois,
    chunk_limit = 25
  ) |>
    openalexPro::pro_request(
      output = file.path(fn, "..", "json_doi")
    ) |>
    openalexPro::pro_request_jsonl(
      output = file.path(fn, "..", "jsonl_doi"),
      delete_input = TRUE
    ) |>
    openalexPro::pro_request_jsonl_parquet(
      output = file.path(fn, "found_in=spc", "id_used=doi"),
      delete_input = TRUE
    )

  openalexPro::pro_query(
    title_and_abstract.search = st$spc,
    id = ids,
    multiple_id = TRUE,
    chunk_limit = 25
  ) |>
    openalexPro::pro_request(output = file.path(fn, "json_oa_id")) |>
    openalexPro::pro_request_jsonl(
      output = file.path(fn, "jsonl_oa_id"),
      delete_input = TRUE
    ) |>
    openalexPro::pro_request_jsonl_parquet(
      output = file.path(fn, "found_in=spc", "id_used=oa_id"),
      delete_input = TRUE
    )

  ### KP in nature
  openalexPro::pro_query(
    title_and_abstract.search = st$nature,
    doi = dois,
    chunk_limit = 25
  ) |>
    openalexPro::pro_request(output = file.path(fn, "json_doi")) |>
    openalexPro::pro_request_jsonl(
      output = file.path(fn, "jsonl_doi"),
      delete_input = TRUE
    ) |>
    openalexPro::pro_request_jsonl_parquet(
      output = file.path(fn, "found_in=nature", "id_used=doi"),
      delete_input = TRUE
    )

  openalexPro::pro_query(
    title_and_abstract.search = st$nature,
    id = ids,
    multiple_id = TRUE,
    chunk_limit = 25
  ) |>
    openalexPro::pro_request(output = file.path(fn, "json_oa_id")) |>
    openalexPro::pro_request_jsonl(
      output = file.path(fn, "jsonl_oa_id"),
      delete_input = TRUE
    ) |>
    openalexPro::pro_request_jsonl_parquet(
      output = file.path(fn, "found_in=nature", "id_used=oa_id"),
      delete_input = TRUE
    )

  ### KP in spcc
  openalexPro::pro_query(
    title_and_abstract.search = st$spcc,
    doi = params$key_papers$goldstandard$DOI[
      params$key_papers$goldstandard$DOI != ""
    ],
    chunk_limit = 25
  ) |>
    openalexPro::pro_request(output = file.path(fn, "json_doi")) |>
    openalexPro::pro_request_jsonl(
      output = file.path(fn, "jsonl_doi"),
      delete_input = TRUE
    ) |>
    openalexPro::pro_request_jsonl_parquet(
      output = file.path(fn, "found_in=spcc", "id_used=doi"),
      delete_input = TRUE
    )

  openalexPro::pro_query(
    title_and_abstract.search = st$spcc,
    id = params$key_papers$goldstandard$openalex_id[
      params$key_papers$goldstandard$openalex_id != ""
    ],
    multiple_id = TRUE,
    chunk_limit = 25
  ) |>
    openalexPro::pro_request(output = file.path(fn, "json_oa_id")) |>
    openalexPro::pro_request_jsonl(
      output = file.path(fn, "jsonl_oa_id"),
      delete_input = TRUE
    ) |>
    openalexPro::pro_request_jsonl_parquet(
      output = file.path(fn, "found_in=spcc", "id_used=oa_id"),
      delete_input = TRUE
    )
}

Keypaper in Search Terms

The in the previous step retrieved works are analysed here to get a table which shows where the key paper occur.

fn <- file.path(params$keyworks, "kp_found_in.rds")
if (!file.exists(fn)) {
  arrow::open_dataset(file.path(params$keyworks, "parquet")) |>
    dplyr::select(
      id,
      doi,
      type,
      found_in,
      title,
      citation
    ) |>
    dplyr::group_by(id, doi, title, citation, type) |>
    dplyr::summarise(
      in_openalex = base::max(found_in == "openalex", na.rm = TRUE),
      in_spc = base::max(found_in == "spc", na.rm = TRUE),
      in_nature = base::max(found_in == "nature", na.rm = TRUE),
      in_spcc = base::max(found_in == "spcc", na.rm = TRUE),
      .groups = "drop"
    ) |>
    dplyr::collect() |>
    saveRDS(fn)
}

Get Numbers from OpenAlex of the Search Terms

These data is gathered from OpenAlex directly, not downloaded any works. The data is used to assess the quality of the TCA Corpus.

The query contains: - the search term (nature, spc, spcc) - the types selected (article, book, book-chapter, dissertation, preprint, report, review) - the date range (from 1992-01-01 to 2025-12-31)

The following counts are retrieved:

Overall hits

#|

fn <- file.path(params$corpus, "st_hits.rds")
if (!file.exists(fn)) {
  st <- list(
    spc = params$search_terms$spc |>
      paste0(collapse = " "),
    nature = params$search_terms$nature |>
      paste0(collapse = " ")
  )
  st$spcc <- paste0("(", st$spc, ") AND (", st$nature, ")")

  queries <- lapply(
    st,
    function(s) {
      openalexPro::pro_query(
        title_and_abstract.search = s,
        type = params$types_filter,
        from_publication_date = params$publication_date$from,
        to_publication_date = params$publication_date$to
      )
    }
  )
  queries$openalex <- openalexPro::pro_query(
    type = params$types_filter,
    from_publication_date = params$publication_date$from,
    to_publication_date = params$publication_date$to,
  )

  pbapply::pblapply(
    queries,
    function(query) {
      query |>
        openalexR::oa_request(
          count_only = TRUE,
          verbose = TRUE
        ) |>
        unlist()
    }
  ) |>
    do.call(what = cbind) |>
    t() |>
    as.data.frame() |>
    dplyr::select(count) |>
    saveRDS(file = fn)
}

Counts per Language

#|

fn <- file.path(params$corpus, "st_languages.rds")
if (!file.exists(fn)) {
  st <- list(
    spc = params$search_terms$spc |>
      paste0(collapse = " "),
    nature = params$search_terms$nature |>
      paste0(collapse = " ")
  )
  st$spcc <- paste0("(", st$spc, ") AND (", st$nature, ")")

  queries <- lapply(
    st,
    function(s) {
      openalexPro::pro_query(
        title_and_abstract.search = s,
        type = params$types_filter,
        from_publication_date = params$publication_date$from,
        to_publication_date = params$publication_date$to,
        group_by = "language"
      )
    }
  )
  queries$openalex <- openalexPro::pro_query(
    type = params$types_filter,
    from_publication_date = params$publication_date$from,
    to_publication_date = params$publication_date$to,
    group_by = "language"
  )

  pbapply::pblapply(
    queries,
    function(query) {
      query |>
        openalexR::oa_request(
          verbose = TRUE
        ) |>
        dplyr::bind_rows()
    }
  ) |>
    dplyr::bind_rows(.id = "source") |>
    dplyr::select(source, language = key_display_name, count) |>
    tidyr::pivot_wider(
      names_from = source,
      values_from = count,
      names_prefix = "count_",
      values_fill = 0
    ) |>
    dplyr::select(
      language,
      count_openalex,
      count_spc,
      count_nature,
      count_spcc
    ) |>
    dplyr::arrange(language) |>
    saveRDS(file = fn)
}

Counts per Publication Year

fn <- file.path(params$corpus, "st_years.rds")
if (!file.exists(fn)) {
  st <- list(
    spc = params$search_terms$spc |>
      paste0(collapse = " "),
    nature = params$search_terms$nature |>
      paste0(collapse = " ")
  )
  st$spcc <- paste0("(", st$spc, ") AND (", st$nature, ")")

  queries <- lapply(
    st,
    function(s) {
      openalexPro::pro_query(
        title_and_abstract.search = s,
        type = params$types_filter,
        from_publication_date = params$publication_date$from,
        to_publication_date = params$publication_date$to,
        group_by = "publication_year"
      )
    }
  )
  queries$openalex <- openalexPro::pro_query(
    type = params$types_filter,
    from_publication_date = params$publication_date$from,
    to_publication_date = params$publication_date$to,
    group_by = "publication_year"
  )

  result <- pbapply::pblapply(
    queries,
    function(query) {
      query |>
        openalexR::oa_request(
          verbose = TRUE
        ) |>
        dplyr::bind_rows()
    }
  ) |>
    dplyr::bind_rows(.id = "source") |>
    dplyr::select(source, year = key, count) |>
    dplyr::mutate(year = base::as.integer(year)) |>
    tidyr::pivot_wider(
      names_from = source,
      values_from = count,
      names_prefix = "count_",
      values_fill = 0
    ) |>
    dplyr::select(
      year,
      count_openalex,
      count_spc,
      count_nature,
      count_spcc
    ) |>
    dplyr::arrange(dplyr::desc(year)) |>
    saveRDS(file = fn)
}

Results

Assessment of Search Terms

SPC Term

readRDS(file.path(params$output, "searchterm_assessment_spcc.rds"))$spc |>
  dplyr::arrange(desc(count)) |>
  dplyr::mutate(
    count = format(count, big.mark = ","),
    count_excl = format(count_excl, big.mark = ","),
  ) |>
  knitr::kable(format = "html", escape = FALSE)

term	count	count_excl
"connectivity"	460,287	453,529
"urban planning"	69,642	61,742
"protected area"	58,642	52,494
"zoning"	47,430	42,878
"spatial planning"	26,415	20,569
"land use planning"	22,834	0
"land-use planning"	22,834	0
"environmental impact assessment"	18,914	17,518
"ecological restoration"	15,844	13,908
"regional planning"	15,617	13,234
"stepping stones"	15,318	14,615
"conservation planning"	13,857	11,018
"planning tools"	13,670	11,846
"spatial configuration"	11,425	10,302
"green infrastructure"	11,246	9,151
"spatial development"	10,486	8,420
"nature based solutions"	7,852	0
"nature-based solutions"	7,852	0
"landscape planning"	6,330	5,121
"ecological networks"	5,787	4,626
"multi-criteria decision analysis"	5,554	5,263
"ecosystem restoration"	4,783	3,857
"spatial transformation"	4,643	4,275
"scenario planning"	4,505	4,212
"habitat restoration"	4,304	3,526
"landscape management"	4,301	3,599
"land use management"	3,900	0
"land-use management"	3,900	0
"territorial planning"	3,774	2,730
"restoration planning"	3,383	2,599
"participatory planning"	3,301	2,740
"strategic environmental assessment"	2,768	1,975
"land allocation"	2,690	2,355
"spatial composition"	2,578	2,369
"ecosystem-based management"	2,372	0
"ecosystem based management"	2,372	0
"forest management planning"	2,362	2,197
"adaptive planning"	2,316	2,153
"land use model"	2,187	0
"land-use model"	2,187	0
"ecological corridor"	2,060	1,068
"rewilding"	2,033	1,790
"land use scenario"	1,974	0
"land-use scenario"	1,974	0
"sectorial planning"	1,735	1,469
"landscape restoration"	1,701	1,373
"integrated coastal zone management"	1,514	1,300
"land governance"	1,422	1,290
"restoration ecology"	1,416	1,036
"inclusive governance"	1,410	1,293
"functional landscapes"	870	738
"wildlife corridor"	767	496
"reserve design"	637	412
"blue infrastructure"	600	363
"spatial governance"	581	398
"working landscapes"	461	395
"marine governance"	387	301
"habitat corridor"	367	198
"community based planning"	335	0
"community-based planning"	335	0
"OECM"	325	223
"ecosystem service model"	282	227
"spatial priority"	279	171
"landscape governance"	276	208
"anticipatory planning"	228	214
"scenario-based planning"	220	0
"scenario based planning"	220	0
"ecosystem service mapping"	138	102
"land use governance"	132	0
"land-use governance"	132	0
"place-based planning"	66	0
"place based planning"	66	0
"spatial conservation priorities"	66	37
"remote ocean areas"	62	58
"sea use management"	38	0
"sea-use management"	38	0
"spatial forest planning"	35	30
"agricultural management planning"	29	29
"ecosystem service planning"	22	14
"ocean use management"	22	21
"land-sea planning"	20	10
"planning across scales"	9	6
"seascape planning"	9	6
"cross-scale planning"	2	1
"biodiversity-inclusive planning"	1	0
"inland waters planning"	1	1
"maretories"	0	0
"seascape governance"	0	0

Nature Term

readRDS(file.path(params$output, "searchterm_assessment_spcc.rds"))$nature |>
  dplyr::arrange(desc(count)) |>
  dplyr::mutate(
    count = format(count, big.mark = ","),
    count_excl = format(count_excl, big.mark = ","),
  ) |>
  knitr::kable(format = "html", escape = FALSE)

term	count	count_excl
"environment"	5,612,124	4,333,552
"species"	3,609,619	2,541,003
"environmental"	3,442,467	2,318,758
"nature"	3,224,734	2,578,060
"sea"	1,249,802	851,316
"conservation"	826,002	422,236
"Earth"	814,952	582,018
"ecosystem"	754,734	335,560
"ocean"	637,143	356,618
"restore"	587,759	430,791
"habitat"	525,847	180,612
"conserve"	491,662	319,042
"restoration"	459,898	311,273
"coast"	404,467	225,036
"ecology"	397,625	193,370
"biodiversity"	272,814	68,087
"planet"	197,755	117,274
"protected area"	58,642	18,872
"biosphere"	42,097	16,117
"biodiverse"	4,211	570
"nature's contribution to people"	232	33
"nature futures framework"	68	0

Keypaper in Corpus

No filtering, neither by type nor by publication year is done. Therefore, the pure search terms are evaluated. If a paper is included in this table, doex not mean it is included in the final SPC Corpus due to filtering by dates and types!

readRDS(file.path(params$keyworks, "kp_found_in.rds")) |>
  dplyr::mutate(
    id_display = sub("^.*/(W[0-9]+)$", "\\1", id),
    id = sprintf("<a href=\"%s\" target=\"_blank\">%s</a>", id, id_display),
    doi_display = sub("^https://doi.org/", "\\1", doi),
    doi = sprintf("<a href=\"%s\" target=\"_blank\">%s</a>", doi, doi_display)
  ) |>
  dplyr::arrange(
    in_spcc,
    in_spc,
    in_nature,
    in_openalex
  ) |>
  dplyr::mutate(
    dplyr::across(
      dplyr::starts_with("in_"),
      ~ dplyr::case_when(
        .x ~ '<b style="color:#008000;">☑</b>', # green bold checkbox
        !.x ~ '<b style="color:#cc0000;">☐</b>' # red bold empty checkbox
      )
    )
  ) |>
  dplyr::select(
    id,
    doi,
    citation,
    in_spcc,
    in_spc,
    in_nature,
    in_openalex
  ) |>
  knitr::kable(format = "html", escape = FALSE)

id	doi	citation	in_spcc	in_spc	in_nature	in_openalex
W4233598570	10.1146/annurev-anthro-102218-011435	David Valentine & Amelia Hassoun (2019)	☐	☐	☐	☑
W2056591496	10.1016/s0016-3287(98)00101-3	Richard A. Slaughter (1998)	☐	☐	☐	☑
W2090312322	10.1016/j.futures.2007.11.010	Joseph Voros (2007)	☐	☐	☐	☑
W1992974326	10.1016/j.futures.2007.11.011	Richard A. Slaughter (2007)	☐	☐	☐	☑
W2884329716	10.1007/978-3-319-94021-2	László Miklós & Anna Špinerová (2018)	☐	☐	☐	☑
W2128217744	10.1177/0306312713508669	David H. Guston (2013)	☐	☐	☐	☑
W2057938345	10.1108/14636680810855991	Sohail Inayatullah (2008)	☐	☐	☐	☑
W1973628253	10.1016/j.enpol.2005.12.006	Will McDowall & Malcolm Eames (2006)	☐	☐	☐	☑
W3170399214	10.1016/j.mex.2021.101401	Daniel Beiderbeck et al. (2021)	☐	☐	☐	☑
W2964311550	10.1016/j.techfore.2019.07.002	Ian Belton et al. (2019)	☐	☐	☐	☑
W4238224782	10.1016/j.futures.2015.08.007	I. Milojević & Sohail Inayatullah (2015)	☐	☐	☐	☑
W2114666631	10.1525/bio.2010.60.3.7	Timothy J. Beechie et al. (2010)	☐	☐	☑	☑
W2062482344	10.1073/pnas.1201040109	Joshua Goldstein et al. (2012)	☐	☐	☑	☑
W4382181311	10.1007/s11625-023-01316-1	América Paz Durán et al. (2023)	☐	☐	☑	☑
W4395447577	10.1126/science.adn3441	Henrique M. Pereira et al. (2024)	☐	☐	☑	☑
W4212886831	10.1111/geb.13459	Karel Mokany et al. (2022)	☐	☐	☑	☑
W2015619301	10.1073/pnas.1000530107	Lian Pin Koh & Jaboury Ghazoul (2010)	☐	☐	☑	☑
W3074014079	10.1007/s10113-020-01685-8	Clara J. Veerkamp et al. (2020)	☐	☐	☑	☑
W4382751812	10.1016/j.biocon.2023.110068	Marcel Kok et al. (2023)	☐	☐	☑	☑
W2972648221	10.1111/1365-2664.13506	Karel Mokany et al. (2019)	☐	☐	☑	☑
W3044450114	10.1016/j.envsoft.2020.104806	Andrew J. Hoskins et al. (2020)	☐	☐	☑	☑
W3145276257	10.1016/j.jenvman.2021.112400	Yuyoung Choi et al. (2021)	☐	☐	☑	☑
W4298615974	10.1007/s10980-022-01534-5	Tom Harwood et al. (2022)	☐	☐	☑	☑
W4414957974	10.1073/pnas.2501695122	Damaris Zurell et al. (2025)	☐	☐	☑	☑
W2255223904	NA	Bob Scholes (2010)	☐	☐	☑	☑
W4295308933	10.1073/pnas.2203385119	Natalia Estrada-Carmona et al. (2022)	☐	☐	☑	☑
W2795786882	10.1007/s00267-018-1028-3	Ida N.S. Djenontin & Alison M. Meadow (2018)	☐	☐	☑	☑
W3215595594	10.1093/biosci/biab091	Abigail J. Lynch et al. (2021)	☐	☐	☑	☑
W4412257196	10.5281/zenodo.6522392	Unai Pascual et al. (2022)	☐	☐	☑	☑
W4415164578	10.1098/rsos.250810	Rachael Garrett et al. (2025)	☐	☐	☑	☑
W2093445010	10.1126/science.1258832	Jianguo Liu et al. (2015)	☐	☐	☑	☑
W3085006993	10.1002/pan3.10146	Laura Pereira et al. (2020)	☐	☐	☑	☑
W2754686867	10.1038/s41559-017-0273-9	Isabel M.D. Rosa et al. (2017)	☐	☐	☑	☑
W4380362703	10.1016/j.gloenvcha.2023.102681	Hyejin Kim et al. (2023)	☐	☐	☑	☑
W2086673960	10.1016/j.tree.2009.04.008	William J. Sutherland & Harry J. Woodroof (2009)	☐	☐	☑	☑
W4210386268	10.1016/j.envsci.2022.01.013	Andressa V. Mansur et al. (2022)	☐	☐	☑	☑
W4210765186	10.1038/s41893-021-00844-x	Roslyn Henry et al. (2022)	☐	☐	☑	☑
W4310004272	10.1007/s11625-022-01251-7	Lucas Rutting et al. (2022)	☐	☐	☑	☑
W4211108983	10.1007/978-3-030-20024-4	Davide Geneletti et al. (2019)	☐	☐	☑	☑
W2904898541	10.1007/s10668-018-00300-5	Azime Tezer et al. (2018)	☐	☐	☑	☑
W4404646473	10.1007/s00267-024-02086-x	Nina Farwig et al. (2024)	☐	☐	☑	☑
W2784805535	10.1126/science.aam9712	Marlee A. Tucker et al. (2018)	☐	☐	☑	☑
W4390696637	10.1038/s41467-023-43832-9	Rachel Neugarten et al. (2024)	☐	☐	☑	☑
W4392367143	10.1111/brv.13066	Steven J. Cooke et al. (2024)	☐	☐	☑	☑
W2161139387	10.1126/science.1196624	Henrique M. Pereira et al. (2010)	☐	☐	☑	☑
W2028797766	10.1016/j.cosust.2013.05.002	Ralf Seppelt et al. (2013)	☐	☐	☑	☑
W2159760863	10.1111/j.1466-8238.2010.00620.x	Joachim H. Spangenberg et al. (2011)	☐	☐	☑	☑
W1990241575	10.1007/s10021-004-0074-2	Paul Raskin (2005)	☐	☐	☑	☑
W2015646994	10.1016/j.tree.2014.07.005	Carly N. Cook et al. (2014)	☐	☐	☑	☑
W2902463689	10.1016/j.tree.2018.10.006	Emily Nicholson et al. (2018)	☐	☐	☑	☑
W4411717980	10.1007/s11625-025-01682-y	Sana Okayasu et al. (2025)	☐	☐	☑	☑
W4386734502	10.1146/annurev-environ-112321-095011	Steven J. Cork et al. (2023)	☐	☐	☑	☑
W3200717663	10.1007/s10980-021-01329-0	Jianquan Dong et al. (2021)	☐	☑	☐	☑
W4407294089	10.1080/02697459.2025.2459975	Romina Rodela (2025)	☐	☑	☐	☑
W3209482460	10.25607/obp-1666	Alejandro Iglesias-Campos et al. (2021)	☐	☑	☐	☑
W2116090915	10.1016/j.futures.2012.10.003	Muhammad Amer et al. (2012)	☐	☑	☐	☑
W4386609980	10.1038/s43588-023-00503-5	Yu Zheng et al. (2023)	☐	☑	☐	☑
W2099188808	10.1016/j.landurbplan.2006.04.005	Jolande W. Termorshuizen et al. (2006)	☐	☑	☐	☑
W2910481941	10.1080/02513625.2018.1562795	Peter Schmitt & Thorsten Wiechmann (2018)	☐	☑	☐	☑
W4410629764	10.1016/j.rsma.2025.104257	Liisi Lees et al. (2025)	☐	☑	☐	☑
W2614376759	10.1016/j.envsci.2017.05.003	Vanessa M. Adams et al. (2017)	☐	☑	☐	☑
W2971398159	10.1111/rec.13035	George D. Gann et al. (2019)	☑	☑	☑	☑
W1540682446	10.1111/brv.12008	Aija S. Kukkala & Atte Moilanen (2012)	☑	☑	☑	☑
W3161819570	10.1126/science.abc4896	Louise O’Connor et al. (2021)	☑	☑	☑	☑
W2952896657	10.5194/gmd-11-4537-2018	Hyejin Kim et al. (2018)	☑	☑	☑	☑
W3151200781	10.1111/rec.13403	Ben L. Gilby et al. (2021)	☑	☑	☑	☑
W3126018057	10.1111/rec.13346	Jordi Cortina et al. (2021)	☑	☑	☑	☑
W3185562566	10.1038/d41586-021-02041-4	Georgina G. Gurney et al. (2021)	☑	☑	☑	☑
W4406904698	10.1016/j.tree.2024.12.002	Sylvaine Giakoumi et al. (2025)	☑	☑	☑	☑
W2769947232	10.1016/j.cosust.2017.10.004	Jean Paul Metzger et al. (2017)	☑	☑	☑	☑
W2978599153	NA	Peter H. Verburg et al. (2019)	☑	☑	☑	☑
W4320016094	10.1007/978-3-031-15773-8_4	Falko Buschke et al. (2023)	☑	☑	☑	☑
W2046569818	10.1007/s10980-014-0052-9	Christine Fürst et al. (2014)	☑	☑	☑	☑
W2766457534	10.1016/j.landusepol.2017.10.017	Chiara Cortinovis & Davide Geneletti (2017)	☑	☑	☑	☑
W2035832288	10.1007/s10980-014-0085-0	Christian Albert et al. (2014)	☑	☑	☑	☑
W2592317409	10.1080/21513732.2017.1296494	Justice Nana Inkoom et al. (2017)	☑	☑	☑	☑
W2612157793	10.1016/j.marpol.2017.06.020	Mara Ntona & Elisa Morgera (2017)	☑	☑	☑	☑
W3134065015	10.1016/j.envsci.2021.02.001	Davide Longato et al. (2021)	☑	☑	☑	☑
W2902345284	10.1007/s10980-018-0745-6	Marcin Spyra et al. (2018)	☑	☑	☑	☑
W3158973852	10.1016/j.landurbplan.2021.104129	Chiara Cortinovis et al. (2021)	☑	☑	☑	☑
W2999493939	10.1016/j.landurbplan.2019.103741	Christian Albert et al. (2020)	☑	☑	☑	☑
W3159060995	10.1016/j.ecoser.2021.101273	Karsten Grunewald et al. (2021)	☑	☑	☑	☑
W2810876831	10.3897/rio.4.e28045	Evelyn Underwood et al. (2018)	☑	☑	☑	☑
W2771003204	10.1080/21513732.2017.1396257	Christine Fürst et al. (2017)	☑	☑	☑	☑
W4376627395	10.1016/j.marpol.2023.105655	Julie Reimer et al. (2023)	☑	☑	☑	☑
W4392293717	10.1016/j.ecolind.2024.111816	Wen Song et al. (2024)	☑	☑	☑	☑
W2804302736	10.1016/j.scitotenv.2018.05.147	Maria da Luz Fernandes et al. (2018)	☑	☑	☑	☑
W4289516997	10.1007/s41207-022-00315-5	Georgia Pozoukidou et al. (2022)	☑	☑	☑	☑
W2604803374	10.1093/biosci/bix012	Charles H. Nilon et al. (2017)	☑	☑	☑	☑
W4406477943	10.1007/s11252-024-01656-5	Israa H. Mahmoud et al. (2025)	☑	☑	☑	☑
W4220936177	10.1016/j.oneear.2022.02.008	Christopher M. Raymond et al. (2022)	☑	☑	☑	☑
W2122104680	10.1111/j.1523-1739.2009.01212.x	William J. Sutherland et al. (2009)	☑	☑	☑	☑
W4211243502	10.1038/35012251	Chris Margules & Robert L. Pressey (2000)	☑	☑	☑	☑
W1963746476	10.1016/j.ecocom.2009.10.006	R.S. de Groot et al. (2009)	☑	☑	☑	☑
W4297536966	10.1016/j.tree.2022.09.002	Maria Beger et al. (2022)	☑	☑	☑	☑
W2956763155	10.1088/1748-9326/ab3234	Annika T. H. Keeley et al. (2019)	☑	☑	☑	☑
W2487200415	10.1016/j.marpol.2016.06.023	Elianny Domínguez-Tejo et al. (2016)	☑	☑	☑	☑
W3100416804	10.1111/1365-2664.13796	Virgilio Hermoso et al. (2020)	☑	☑	☑	☑
W4313593374	10.1016/j.mex.2022.101989	Holly Kirk et al. (2023)	☑	☑	☑	☑
W2554309037	10.1111/btp.12386	Agnieszka E. Latawiec et al. (2016)	☑	☑	☑	☑
W2759207970	10.3390/su9091668	Leena Karrasch et al. (2017)	☑	☑	☑	☑
W4393866290	10.3390/su16072965	Qiqi Hu et al. (2024)	☑	☑	☑	☑
W4387055605	10.1038/s44183-023-00022-w	Julie Reimer et al. (2023)	☑	☑	☑	☑
W4281717750	10.1126/science.abl8974	Angela Brennan et al. (2022)	☑	☑	☑	☑
W2918948909	10.1016/j.gecco.2019.e00569	Arieanna C. Balbar & Anna Meta×as (2019)	☑	☑	☑	☑
W1966247992	10.1080/21513732.2011.617711	Davide Geneletti (2011)	☑	☑	☑	☑
W2766942546	10.1080/08920753.2017.1373450	Kekuewa Kikiloi et al. (2017)	☑	☑	☑	☑
W2265414043	10.1146/annurev.ecolsys.32.081501.114012	Steward T. A. Pickett et al. (2001)	☑	☑	☑	☑
W2027491594	10.1016/j.ecolind.2015.03.029	Christian Albert et al. (2015)	☑	☑	☑	☑
W4409833817	10.1126/science.adn2225	Jedediah F. Brodie et al. (2025)	☑	☑	☑	☑
W2524916285	10.1002/aqc.2645	Alan M. Friedlander et al. (2016)	☑	☑	☑	☑
W3194313759	10.1007/978-94-024-1681-7	Christina von Haaren et al. (2019)	☑	☑	☑	☑
W3021127236	10.1016/j.marpol.2020.103950	Thomas Robertson et al. (2020)	☑	☑	☑	☑
W4412750811	10.1016/j.marpol.2025.106852	Jean‐Eudes Beuret et al. (2025)	☑	☑	☑	☑
W4413900892	10.1016/j.tree.2025.07.014	Jian Peng et al. (2025)	☑	☑	☑	☑
W2783683111	10.1016/j.biocon.2017.12.020	Santiago Saura et al. (2018)	☑	☑	☑	☑
W1880093763	10.1111/ecog.01507	Rafael A. Magris et al. (2015)	☑	☑	☑	☑
W3132073286	10.1016/j.biocon.2021.109008	Annika T. H. Keeley et al. (2021)	☑	☑	☑	☑
W2965201645	10.1016/j.biocon.2019.07.028	Santiago Saura et al. (2019)	☑	☑	☑	☑
W3085078608	10.1038/s41467-020-18457-x	Michelle Ward et al. (2020)	☑	☑	☑	☑
W2791599583	10.1111/conl.12439	Rafael A. Magris et al. (2018)	☑	☑	☑	☑
W4302293680	10.1111/csp2.12823	David M. Theobald et al. (2022)	☑	☑	☑	☑
W1990273831	10.1126/science.1242552	Silke Bauer & Bethany J. Hoye (2014)	☑	☑	☑	☑
W4412704230	10.1073/pnas.2410937122	Robin Naidoo et al. (2025)	☑	☑	☑	☑
W4280541891	10.3389/fevo.2022.830822	Sylvia Wood et al. (2022)	☑	☑	☑	☑
W4413449508	10.1038/s41467-025-63205-8	Jedediah F. Brodie et al. (2025)	☑	☑	☑	☑
W4402144060	10.1002/ece3.70231	Amanda Liczner et al. (2024)	☑	☑	☑	☑
W4414925393	10.1016/j.tree.2025.09.007	Sandra Neubert et al. (2025)	☑	☑	☑	☑
W4410131928	10.3354/meps14888	Susanne E. Tanner et al. (2025)	☑	☑	☑	☑
W4415240687	10.1007/s10980-025-02210-0	Tamsin L. Woodman et al. (2025)	☑	☑	☑	☑
W2023339029	10.1046/j.1523-1739.2003.01491.x	Garry Peterson et al. (2003)	☑	☑	☑	☑
W2470861343	10.1016/j.landurbplan.2016.05.003	Adrienne Grêt‐Regamey et al. (2016)	☑	☑	☑	☑
W56780107	10.1007/978-1-4612-0529-6_10	Jack Ahern (1999)	☑	☑	☑	☑
W2073677603	10.1016/j.biocon.2015.02.015	Sebastián Martinuzzi et al. (2015)	☑	☑	☑	☑
W2156111479	10.1111/gcb.12383	Sebastián Martinuzzi et al. (2013)	☑	☑	☑	☑
W4406487214	10.1007/s10980-024-02042-4	Jiangxiao Qiu et al. (2025)	☑	☑	☑	☑

SPC Corpus Measures and Numbers

These data is gathered from OpenAlex directly, not downloaded any works. The data is used to assess the quality of the TCA Corpus.

The query contains: - the search term (nature, spc, spcc) - the types selected (article, book, book-chapter, dissertation, preprint, report, review) - the date range (from 1992-01-01 to 2025-12-31)

Overall counts

readRDS(file.path(params$corpus, "st_hits.rds")) |>
  dplyr::mutate(
    count = format(count, big.mark = ",")
  ) |>
  knitr::kable(format = "html", escape = FALSE)

	count
spc	817,150
nature	16,831,704
spcc	316,998
openalex	205,618,381

Publication Years

readRDS(file.path(params$corpus, "st_years.rds")) |>
  dplyr::mutate(
    dplyr::across(
      dplyr::starts_with("count_"),
      ~ base::format(.x, big.mark = ",")
    )
  ) |>
  knitr::kable(format = "html", escape = FALSE)

year	count_openalex	count_spc	count_nature	count_spcc
2025	6,238,678	47,767	740,853	22,345
2024	8,628,798	58,341	1,018,186	25,573
2023	8,698,025	57,058	1,028,635	23,714
2022	7,751,702	51,353	923,218	20,894
2021	8,698,617	53,218	942,833	21,035
2020	9,542,282	50,111	914,885	19,012
2019	9,282,492	44,645	816,583	16,741
2018	9,060,432	40,817	756,636	15,150
2017	9,008,090	37,669	705,257	13,827
2016	9,196,091	35,877	688,657	13,261
2015	8,969,721	34,209	681,556	12,660
2014	8,878,581	33,531	682,758	12,835
2013	8,605,745	31,662	660,254	11,940
2012	8,143,521	28,659	618,795	11,213
2011	7,877,400	26,595	582,558	10,126
2010	7,344,239	23,915	539,602	9,129
2009	6,872,587	20,705	487,532	7,965
2008	6,358,201	18,358	443,181	6,965
2007	5,911,130	16,187	403,977	6,102
2006	5,574,062	14,510	377,466	5,265
2005	5,105,616	13,063	343,339	4,903
2004	4,673,271	11,121	309,091	4,069
2003	4,343,325	10,371	287,303	3,768
2002	4,152,643	9,772	283,751	3,309
2001	3,562,560	7,275	221,308	2,670
2000	3,394,884	6,707	208,037	2,322
1999	2,945,662	5,389	180,150	1,843
1998	2,808,879	4,977	168,916	1,624
1997	2,679,761	4,544	158,089	1,481
1996	2,551,167	4,511	152,580	1,397
1995	2,375,803	3,950	140,460	1,174
1994	2,238,823	3,632	128,767	996
1993	2,123,181	3,387	121,619	866
1992	2,021,948	3,113	112,620	745

This graph only shows the relative nuymber of publications per year to identify different trends.

readRDS(file.path(params$corpus, "st_years.rds")) |>
  tidyr::pivot_longer(
    cols = dplyr::starts_with("count_"),
    names_to = "source",
    values_to = "count",
    names_prefix = "count_"
  ) |>
  # scale each source so its total sum is 1
  dplyr::group_by(source) |>
  dplyr::mutate(
    total_count = base::sum(count, na.rm = TRUE),
    count = dplyr::if_else(total_count > 0, count / total_count, 0)
  ) |>
  dplyr::ungroup() |>
  dplyr::select(-total_count) |>
  ggplot2::ggplot(ggplot2::aes(x = year, y = count, color = source)) +
  ggplot2::geom_line(linewidth = 1) +
  ggplot2::geom_point(size = 1.5) +
  ggplot2::scale_y_continuous(
    labels = scales::label_percent(accuracy = 1),
    expand = ggplot2::expansion(mult = c(0.02, 0.06))
  ) +
  ggplot2::labs(
    x = "Year",
    y = "Share of total works (sum-scaled per source)",
    color = "Source",
    title = "Publications per year by source (each source sums to 1)"
  ) +
  ggplot2::theme_minimal(base_size = 14) +
  ggplot2::theme(
    legend.position = "bottom",
    panel.grid.minor = ggplot2::element_blank()
  )

Language

readRDS(file.path(params$corpus, "st_languages.rds")) |>
  dplyr::mutate(
    dplyr::across(
      dplyr::starts_with("count_"),
      ~ base::format(.x, big.mark = ",")
    )
  ) |>
  knitr::kable(format = "html", escape = FALSE)

language	count_openalex	count_spc	count_nature	count_spcc
Afrikaans	151,411	39	1,352	16
Albanian	13,441	1	59	0
Arabic	629,566	88	3,257	35
Bengali	2,831	2	39	2
Bulgarian	52,148	22	625	7
Catalan	546,849	389	8,458	274
Chinese	4,511,228	263	5,240	39
Croatian	327,706	187	2,572	41
Czech	411,580	42	1,033	14
Danish	271,858	41	1,408	5
Dutch	759,899	150	4,028	29
English	146,688,929	797,903	16,209,547	309,623
Estonian	107,259	21	989	11
Finnish	172,516	21	708	7
French	5,674,578	1,642	172,279	691
German	5,116,198	794	15,167	208
Gujarati	433	0	1	0
Hebrew	3,845	7	99	5
Hindi	10,134	0	22	0
Hungarian	132,278	72	1,863	18
Indonesian	3,063,755	3,781	80,917	1,318
Italian	1,479,762	570	9,394	208
Japanese	6,356,532	499	6,687	65
Kannada	383	0	0	0
Korean	3,772,022	575	8,497	95
Latvian	25,349	2	91	0
Lithuanian	95,891	80	1,018	33
Macedonian	27,115	10	157	2
Malayalam	308	0	6	0
Marathi	2,225	0	5	0
Modern Greek (1453-)	139,854	73	912	15
Nepali (macrolanguage)	3,418	0	52	0
Norwegian	251,962	34	776	5
Panjabi	117	0	0	0
Persian	493,316	101	1,193	36
Polish	1,118,905	159	3,311	51
Portuguese	3,984,017	1,615	33,446	903
Romanian	257,461	47	2,103	19
Russian	2,266,943	2,051	38,678	666
Slovak	51,220	13	201	8
Slovenian	104,045	70	730	29
Somali	19,536	2	36	1
Spanish	7,138,372	3,851	166,248	1,946
Swahili (macrolanguage)	24,069	2	40	1
Swedish	430,826	46	1,297	13
Tagalog	90,089	67	411	2
Tamil	3,049	0	80	0
Telugu	64	0	2	0
Thai	95,819	137	3,340	54
Turkish	868,254	224	5,108	64
Ukrainian	507,916	250	6,002	92
Urdu	2,608	0	4	0
Vietnamese	164,476	61	1,124	14
Welsh	42,793	4	101	1
NA	26,846	2	15	0

This graph only shows the relative number of publications per year to identify different trends.

readRDS(file.path(params$corpus, "st_languages.rds")) |>
  # keep top 15 languages by total (before scaling)
  dplyr::mutate(
    total = count_openalex + count_spc + count_nature + count_spcc
  ) |>
  dplyr::slice_max(total, n = 15) |>
  dplyr::arrange(dplyr::desc(total)) |>
  # fix display order so largest stays on top
  dplyr::mutate(language = factor(language, levels = rev(language))) |>
  dplyr::select(-total) |>
  # reshape wide → long
  tidyr::pivot_longer(
    cols = dplyr::starts_with("count_"),
    names_to = "source",
    values_to = "count",
    names_prefix = "count_"
  ) |>
  # scale so each source sums to 1
  dplyr::group_by(source) |>
  dplyr::mutate(
    total_source = base::sum(count, na.rm = TRUE),
    count = dplyr::if_else(total_source > 0, count / total_source, 0)
  ) |>
  dplyr::ungroup() |>
  dplyr::select(-total_source) |>
  ggplot2::ggplot(ggplot2::aes(x = language, y = count, fill = source)) +
  ggplot2::geom_col(position = "dodge") +
  ggplot2::coord_flip() +
  ggplot2::scale_y_continuous(
    labels = scales::label_percent(accuracy = 1),
    expand = ggplot2::expansion(mult = c(0, 0.05))
  ) +
  ggplot2::labs(
    x = "Language",
    y = "Share of total works within each source",
    fill = "Source",
    title = "Publications by language (top 15), scaled so each source sums to 1"
  ) +
  ggplot2::theme_minimal(base_size = 14) +
  ggplot2::theme(
    legend.position = "bottom",
    panel.grid.minor = ggplot2::element_blank()
  )